Academic literature on the topic 'Prédiction des séries temporelles multivariées'
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Journal articles on the topic "Prédiction des séries temporelles multivariées":
El-Jabi, N., G. Le-Kourdahi, and D. Caissie. "Modélisation stochastique de la température de l'eau en rivière." Revue des sciences de l'eau 8, no. 1 (April 12, 2005): 77–95. http://dx.doi.org/10.7202/705214ar.
Bar, N. A., M. Boulama-Jackou, and R. Michel. "Utilisation des analyses de séries temporelles dans la prédiction des épidémies de méningites à méningocoques au Niger." Revue d'Épidémiologie et de Santé Publique 58 (September 2010): S92. http://dx.doi.org/10.1016/j.respe.2010.06.144.
Bélanger, M., N. El-Jabi, D. Caissie, F. Ashkar, and J. M. Ribi. "Estimation de la température de l'eau de rivière en utilisant les réseaux de neurones et la régression linéaire multiple." Revue des sciences de l'eau 18, no. 3 (April 12, 2005): 403–21. http://dx.doi.org/10.7202/705565ar.
Bekkis, Soumeya, Mohamed Amine Benmehaia, and Ahcène Kaci. "Les enjeux de la dépendance de la filière de blé en Algérie : Analyse par asymétries de réponses de l’offre dans la chaîne de valeur." New Medit 21, no. 1 (March 31, 2022). http://dx.doi.org/10.30682/nm2201h.
Dissertations / Theses on the topic "Prédiction des séries temporelles multivariées":
Hmamouche, Youssef. "Prédiction des séries temporelles larges." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Nowadays, storage and data processing systems are supposed to store and process large time series. As the number of variables observed increases very rapidly, their prediction becomes more and more complicated, and the use of all the variables poses problems for classical prediction models.Univariate prediction models are among the first models of prediction. To improve these models, the use of multiple variables has become common. Thus, multivariate models and become more and more used because they consider more information.With the increase of data related to each other, the application of multivariate models is also questionable. Because the use of all existing information does not necessarily lead to the best predictions. Therefore, the challenge in this situation is to find the most relevant factors among all available data relative to a target variable.In this thesis, we study this problem by presenting a detailed analysis of the proposed approaches in the literature. We address the problem of prediction and size reduction of massive data. We also discuss these approaches in the context of Big Data.The proposed approaches show promising and very competitive results compared to well-known algorithms, and lead to an improvement in the accuracy of the predictions on the data used.Then, we present our contributions, and propose a complete methodology for the prediction of wide time series. We also extend this methodology to big data via distributed computing and parallelism with an implementation of the prediction process proposed in the Hadoop / Spark environment
Labiadh, Mouna. "Méthodologie de construction de modèles adaptatifs pour la simulation énergétique des bâtiments." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSE1158.
Predictive modeling of energy consumption in buildings is essential for intelligent control and efficient planning of energy networks. One way to perform predictive modeling is through machine learning approaches. Alongside their good performance, these approaches are time efficient and facilitates the integration of buildings into smart environments. However, accurate machine learning models rely heavily on collecting relevant building operational data in a sufficient amount, notably when deep learning is used. In the field of buildings energy, historical data are not available for training, such is the case in newly built or newly renovated buildings. Moreover, it is common to verify the energy efficiency of buildings before construction or renovation. For such cases, only a contextual description about the future building and its design is available. The goal of this dissertation is to address the predictive modeling tasks of building energy consumption when no historical data are available for the given target building. To that end, existing data collected from multiple different source buildings are leveraged. This is increasingly relevant with the growth of open data initiatives in various sectors, namely building energy. The main idea is to transfer knowledge across building models. There is little research at the intersection of building energy modeling and knowledge transfer. An important challenge arises when dealing with multi-source data, since large domain shift may exist between different sources and also between each source and the target. As a contribution, a two-fold query-adaptive methodology is developed for cross-building predictive modeling. The first process recommends relevant training data to a target building solely by using a minimal contextual description on it (metadata). Contextual descriptions are provided as user queries. To enable a task-specific recommendation, a deep similarity learning framework is used. The second process trains multiple predictive models based on recommended training data. These models are combined together using an ensemble learning framework to ensure a robust performance. The implementation of the proposed methodology is based on microservices. Logically independent workflows are modeled as microservices with single purposes and separate data sources. Building metadata and time series data collected from multiple sources are integrated into an unified ontology-based view. Experimental evaluation of the predictive model factory validates the effectiveness and the applicability for the use case of building energy modeling. Moreover, because of its generic design, the methodology for query-adaptive cross-domain predictive modeling can be re-used for a diverse range of use cases in different fields
Guerre, Emmanuel. "Méthode non paramétriques d'analyse des séries temporelles multivariées : estimation de mesures de dépendances." Paris 6, 1993. http://www.theses.fr/1993PA066110.
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066324/document.
This thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
Harlé, Flore. "Détection de ruptures multiples dans des séries temporelles multivariées : application à l'inférence de réseaux de dépendance." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT043/document.
This thesis presents a method for the multiple change-points detection in multivariate time series, and exploits the results to estimate the relationships between the components of the system. The originality of the model, called the Bernoulli Detector, relies on the combination of a local statistics from a robust test, based on the computation of ranks, with a global Bayesian framework. This non parametric model does not require strong hypothesis on the distribution of the observations. It is applicable without modification on gaussian data as well as data corrupted by outliers. The detection of a single change-point is controlled even for small samples. In a multivariate context, a term is introduced to model the dependencies between the changes, assuming that if two components are connected, the events occurring in the first one tend to affect the second one instantaneously. Thanks to this flexible model, the segmentation is sensitive to common changes shared by several signals but also to isolated changes occurring in a single signal. The method is compared with other solutions of the literature, especially on real datasets of electrical household consumption and genomic measurements. These experiments enhance the interest of the model for the detection of change-points in independent, conditionally independent or fully connected signals. The synchronization of the change-points within the time series is finally exploited in order to estimate the relationships between the variables, with the Bayesian network formalism. By adapting the score function of a structure learning method, it is checked that the independency model that describes the system can be partly retrieved through the information given by the change-points, estimated by the Bernoulli Detector
Coelho, rodrigues Pedro Luiz. "Exploration des invariances de séries temporelles multivariées via la géométrie Riemannienne : validation sur des données EEG." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT095.
Multivariate time series are the standard tool for describing and analysing measurements from multiple sensors during an experiment. In this work, we discuss different aspects of such representations that are invariant to transformations occurring in practical situations. The main source of inspiration for our investigations are experiments with neural signals from electroencephalography (EEG), but the ideas that we present are amenable to other kinds of time series.The first invariance that we consider concerns the dimensionality of the multivariate time series. Very often, signals recorded from neighbouring sensors present strong statistical dependency between them. We present techniques for disposing of the redundancy of these correlated signals and obtaining new multivariate time series that represent the same phenomenon but in a smaller dimension.The second invariance that we treat is related to time series describing the same phenomena but recorded under different experimental conditions. For instance, signals recorded with the same experimental apparatus but on different days of the week, different test subjects, etc. In such cases, despite an underlying variability, the multivariate time series share certain commonalities that can be exploited for joint analysis. Moreover, reusing information already available from other datasets is a very appealing idea and allows for “data-efficient” machine learning methods. We present an original transfer learning procedure that transforms these time series so that their statistical distributions become aligned and can be pooled together for further statistical analysis.Finally, we extend the previous case to when the time series are obtained from different experimental conditions and also different experimental setups. A practical example is having EEG recordings from subjects executing the same cognitive task but with the electrodes positioned differently. We present an original method that transforms these multivariate time series so that they become compatible in terms of dimensionality and also in terms of statistical distributions.We illustrate the techniques described above on EEG epochs recorded during brain-computer interface (BCI) experiments. We show examples where the reduction of the multivariate time series does not affect the performance of statistical classifiers used to distinguish their classes, as well as instances where our transfer learning and dimension-matching proposals provide remarkable results on classification in cross-session and cross-subject settings.For exploring the invariances presented above, we rely on a framework that parametrizes the statistics of the multivariate time series via Hermitian positive definite (HPD) matrices. We manipulate these matrices by considering them in a Riemannian manifold in which an adequate metric is chosen. We use concepts from Riemannian geometry to define notions such as geodesic distance, center of mass, and statistical classifiers for time series. This approach is rooted on fundamental results of differential geometry for Hermitian positive definite matrices and has links with other well established areas in applied mathematics, such as information geometry and signal processing
Plaud, Angéline. "Classification ensembliste des séries temporelles multivariées basée sur les M-histogrammes et une approche multi-vues." Thesis, Université Clermont Auvergne (2017-2020), 2019. http://www.theses.fr/2019CLFAC047.
Recording measurements about various phenomena and exchanging information about it, participate in the emergence of a type of data called time series. Today humongous quantities of those data are often collected. A time series is characterized by numerous points and interactions can be observed between those points. A time series is multivariate when multiple measures are recorded at each timestamp, meaning a point is, in fact, a vector of values. Even if univariate time series, one value at each timestamp, are well-studied and defined, it’s not the case of multivariate one, for which the analysis is still challenging. Indeed, it is not possible to apply directly techniques of classification developed on univariate data to the case of multivariate one. In fact, for this latter, we have to take into consideration the interactions not only between points but also between dimensions. Moreover, in industrial cases, as in Michelin company, the data are big and also of different length in terms of points size composing the series. And this brings a new complexity to deal with during the analysis. None of the current techniques of classifying multivariate time series satisfies the following criteria, which are a low complexity of computation, dealing with variation in the number of points and good classification results. In our approach, we explored a new tool, which has not been applied before for MTS classification, which is called M-histogram. A M-histogram is a visualization tool using M axis to project the density function underlying the data. We have employed it here to produce a new representation of the data, that allows us to bring out the interactions between dimensions. Searching for links between dimensions correspond particularly to a part of learning techniques called multi-view learning. A view is an extraction of dimensions of a dataset, which are of same nature or type. Then the goal is to display the links between the dimensions inside each view in order to classify all the data, using an ensemble classifier. So we propose a multi-view ensemble model to classify multivariate time series. The model creates multiple M-histograms from differents groups of dimensions. Then each view allows us to get a prediction which we can aggregate to get a final prediction. In this thesis, we show that the proposed model allows a fast classification of multivariate time series of different sizes. In particular, we applied it on aMichelin use case
Vroman, Philippe. "Prédiction des séries temporelles en milieu incertain : application à la prévision de ventes dans la distribution textile." Lille 1, 2000. http://www.theses.fr/2000LIL10207.
Arnoux, Thibaud. "Prédiction d'interactions dans les flots de liens. Combiner les caractéristiques structurelles et temporelles." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS229.
The link stream formalism represent an approach allowing to capture the system dynamic while providing a framework to understand the system's behavior. A link stream is a sequence of triplet (t,u,v) indicating that an interaction occurred between u and v at time t. The importance of the system's dynamic during the prediction places it at the crossroads of link prediction in graphs and time series prediction. We will explore several formalizations of the problem of prediction in link streams. In the following we will study the activity prediction, that is to say predicting the number of interactions occurring in the future between each pair of nodes during a given period. We introduce the protocol, allowing to combine the data characteristics to predict the activity. We study the behavior of our protocol during several experiments on four datasets et evaluate the prediction quality. We will look at how the introduction of pair of nodes classes allows to preserve the link diversity in the prediction while improving the prediction. Our goal is to define a general prediction framework allowing in-depth studies of the relationship between temporal and structural characteristics in prediction tasks
Ahmad, Ali. "Contribution à l'économétrie des séries temporelles à valeurs entières." Thesis, Lille 3, 2016. http://www.theses.fr/2016LIL30059/document.
The framework of this PhD dissertation is the conditional mean count time seriesmodels. We propose the Poisson quasi-maximum likelihood estimator (PQMLE) for the conditional mean parameters. We show that, under quite general regularityconditions, this estimator is consistent and asymptotically normal for a wide classeof count time series models. Since the conditional mean parameters of some modelsare positively constrained, as, for example, in the integer-valued autoregressive (INAR) and in the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH), we study the asymptotic distribution of this estimator when the parameter lies at the boundary of the parameter space. We deduce a Waldtype test for the significance of the parameters and another Wald-type test for the constance of the conditional mean. Subsequently, we propose a robust and general goodness-of-fit test for the count time series models. We derive the joint distribution of the PQMLE and of the empirical residual autocovariances. Then, we deduce the asymptotic distribution of the estimated residual autocovariances and also of a portmanteau test. Finally, we propose the PQMLE for estimating, equation-by-equation (EbE), the conditional mean parameters of a multivariate time series of counts. By using slightly different assumptions from those given for PQMLE, we show the consistency and the asymptotic normality of this estimator for a considerable variety of multivariate count time series models